Advertising-algorithm-competition | 2018、2019 腾讯广告算法大赛/2018IJCAI 阿里妈妈搜索广告转化预测竞赛/讯飞广告营销算法/OGeek

by wangle1218 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | Advertising-algorithm-competition Summary

Advertising-algorithm-competition is a Python library. Advertising-algorithm-competition has no bugs, it has no vulnerabilities and it has low support. However Advertising-algorithm-competition build file is not available. You can download it from GitHub.

2018、2019 腾讯广告算法大赛/2018IJCAI 阿里妈妈搜索广告转化预测竞赛/讯飞广告营销算法/OGeek

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Quality

Security

License

Reuse

Support

Advertising-algorithm-competition has a low active ecosystem.

It has 161 star(s) with 53 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 497 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Advertising-algorithm-competition is current.

Quality

Advertising-algorithm-competition has 0 bugs and 0 code smells.

Security

Advertising-algorithm-competition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Advertising-algorithm-competition code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Advertising-algorithm-competition does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Advertising-algorithm-competition releases are not available. You will need to build from source code and install.

Advertising-algorithm-competition has no build file. You will be need to create the build yourself to build the component from source.

Advertising-algorithm-competition saves you 1845 person hours of effort in developing the same functionality from scratch.

It has 4072 lines of code, 141 functions and 25 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Advertising-algorithm-competition and discovered the below as its top functions. This is intended to give you an instant insight into Advertising-algorithm-competition implemented functionality, and help decide if they suit your requirements.

Generate features
Fit the model
Evaluate the model
Runs the prediction function
Generate features
Compute mean expo time
Calculates the winrations for a given window
Calculate the cover range number
Runs the base model
Parse features from a csv file
Make feature data
Merge two subsampled data
Generate the feature features
Get seed group
Parse features from input file
Merge the css data
Extract user text features from a dataframe
Generate a feature
Combine combian features
Get result from testcase
Load test data
Performs xgb prediction
Perform lg prediction
Concatenate training data
Wrapper for LgBCV prediction
Generate features for training test
Generate fm data
Get histogram data

Get all kandi verified functions for this library.

Advertising-algorithm-competition Key Features

No Key Features are available at this moment for Advertising-algorithm-competition.

Advertising-algorithm-competition Examples and Code Snippets

No Code Snippets are available at this moment for Advertising-algorithm-competition.

Community Discussions

No Community Discussions are available at this moment for Advertising-algorithm-competition.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Advertising-algorithm-competition

You can download it from GitHub.
You can use Advertising-algorithm-competition like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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